Heatwaves and grapevine yield in the Douro region, crop model simulations

Quercetin precipitation has become an increasingly common issue in red wine, often resulting in visually unpleasant sediments and diminished product quality.

Context and purpose of the study. Intra-varietal variability for key physiological and oenologically important traits can be exploit in viticulture following the consistently higher environmental pressure driven by climate change.

Grapevine interspecific hybrids (PIWIs, from German “pilzwiderstandsfähige Rebsorten” meaning fungus tolerant grapevine cultivars), offer a promising alternative to traditional cultivars in many wine regions due to their tolerance to certain fungal diseases. This makes them a potential solution for sustainable wine production, particularly under organic systems. Despite extensive research on certain agronomic traits and susceptibility to biotic diseases, such as powdery and downy mildews, the response of these cultivars to abiotic stressors, such as drought, remains unclear. Our study aims to investigate the eco-physiological traits of two commercial PIWI cultivars, Muscaris and Souvignier gris, at the leaf level to evaluate their response to drought stress.

Downy mildew, caused by Plasmopara viticola, is the most economically impactful disease affecting grapevines. This polycyclic pathogen triggers both primary and secondary infection cycles, resulting in significant yield losses when effective disease control measures are lacking. Over the winter, the pathogen survives by forming resting structures, the oospores, derived from sexual reproduction, which produce the inoculum for primary infections. To optimize grapevine downy mildew control and obtain the desired levels of production while minimizing chemical inputs, it is crucial to optimize the timeframe for fungicide application. Disease forecasting models are useful to identify the infection risk.

The need to rationalize agricultural inputs has recently increased interest in assessing vineyard variability in order to implement variable rate input applications, so-called ‘precision viticulture’. In many viticultural areas globally, precision viticulture is already widely used such as for selective harvesting and variable rate application (VRA) of inputs such as irrigation and/or fertilizer. Robust VRA relies on having a geostatistically accurate map (of one or more vineyard attributes) requiring high sampling densities, which can be cost- and time-prohibitive to obtain. Previous work on spatial interpolation using kriging have upscaled ground-based measurements, but such upscaling strategies are applicable only when vineyard conditions are spatially continuous and satisfies the assumption of second-order stationary processes. Alternatively, mixed models that combine kriging and auxiliary information, such as the regression kriging (RK) method, are more instructive for spatial predictions. In order to improve prediction accuracies, it is therefore necessary to incorporate additional information to achieve accurate spatial patterns with low error.